{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 程序说明\n",
    "时间：2016年11月23日\n",
    "\n",
    "说明：一个自编码器的例程。\n",
    "1. 单隐藏层的自编码器\n",
    "2. 稀疏约束的自编码器\n",
    "3. 多隐藏层的自编码器\n",
    "\n",
    "数据集：MNIST\n",
    "\n",
    "原博客地址：[Building Autoencoders in Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n",
    "\n",
    "\n",
    "![autoencoders](https://blog.keras.io/img/ae/autoencoder_schema.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.加载keras模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.构建单隐藏层的自编码器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda/lib/python2.7/site-packages/ipykernel_launcher.py:12: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=Tensor(\"de..., inputs=Tensor(\"in...)`\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "# this is the size of our encoded representations\n",
    "encoding_dim = 32  # 32 floats -> compression of factor 24.5, assuming the input is 784 floats\n",
    "\n",
    "# this is our input placeholder\n",
    "input_img = Input(shape=(784,))\n",
    "# \"encoded\" is the encoded representation of the input\n",
    "encoded = Dense(encoding_dim, activation='relu')(input_img)\n",
    "# \"decoded\" is the lossy reconstruction of the input\n",
    "decoded = Dense(784, activation='sigmoid')(encoded)\n",
    "\n",
    "# this model maps an input to its reconstruction\n",
    "autoencoder = Model(input=input_img, output=decoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义编码器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda/lib/python2.7/site-packages/ipykernel_launcher.py:2: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=Tensor(\"de..., inputs=Tensor(\"in...)`\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# this model maps an input to its encoded representation\n",
    "encoder = Model(input=input_img, output=encoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义解码器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda/lib/python2.7/site-packages/ipykernel_launcher.py:6: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=Tensor(\"de..., inputs=Tensor(\"in...)`\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# create a placeholder for an encoded (32-dimensional) input\n",
    "encoded_input = Input(shape=(encoding_dim,))\n",
    "# retrieve the last layer of the autoencoder model\n",
    "decoder_layer = autoencoder.layers[-1]\n",
    "# create the decoder model\n",
    "decoder = Model(input=encoded_input, output=decoder_layer(encoded_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "(x_train, _), (x_test, _) = mnist.load_data()\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n",
    "x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\n",
    "print x_train.shape\n",
    "print x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda/lib/python2.7/site-packages/ipykernel_launcher.py:5: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.3676 - val_loss: 0.2724\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.2649 - val_loss: 0.2544\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.2448 - val_loss: 0.2328\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.2242 - val_loss: 0.2136\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.2081 - val_loss: 0.2003\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1968 - val_loss: 0.1907\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1884 - val_loss: 0.1833\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1815 - val_loss: 0.1770\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1756 - val_loss: 0.1714\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1704 - val_loss: 0.1665\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1657 - val_loss: 0.1620\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1613 - val_loss: 0.1577\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1573 - val_loss: 0.1539\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1536 - val_loss: 0.1505\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1503 - val_loss: 0.1471\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1472 - val_loss: 0.1442\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1444 - val_loss: 0.1416\n",
      "Epoch 18/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1418 - val_loss: 0.1391\n",
      "Epoch 19/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1395 - val_loss: 0.1367\n",
      "Epoch 20/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1372 - val_loss: 0.1347\n",
      "Epoch 21/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1351 - val_loss: 0.1326\n",
      "Epoch 22/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1331 - val_loss: 0.1306\n",
      "Epoch 23/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1312 - val_loss: 0.1287\n",
      "Epoch 24/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1293 - val_loss: 0.1269\n",
      "Epoch 25/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1275 - val_loss: 0.1252\n",
      "Epoch 26/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1258 - val_loss: 0.1235\n",
      "Epoch 27/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1241 - val_loss: 0.1218\n",
      "Epoch 28/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1225 - val_loss: 0.1202\n",
      "Epoch 29/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1209 - val_loss: 0.1187\n",
      "Epoch 30/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1194 - val_loss: 0.1172\n",
      "Epoch 31/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1180 - val_loss: 0.1158\n",
      "Epoch 32/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1167 - val_loss: 0.1145\n",
      "Epoch 33/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1154 - val_loss: 0.1133\n",
      "Epoch 34/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1143 - val_loss: 0.1122\n",
      "Epoch 35/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1132 - val_loss: 0.1111\n",
      "Epoch 36/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1122 - val_loss: 0.1101\n",
      "Epoch 37/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1112 - val_loss: 0.1092\n",
      "Epoch 38/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1104 - val_loss: 0.1084\n",
      "Epoch 39/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1096 - val_loss: 0.1076\n",
      "Epoch 40/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1088 - val_loss: 0.1069\n",
      "Epoch 41/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1081 - val_loss: 0.1062\n",
      "Epoch 42/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1075 - val_loss: 0.1056\n",
      "Epoch 43/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1069 - val_loss: 0.1050\n",
      "Epoch 44/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1063 - val_loss: 0.1044\n",
      "Epoch 45/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1058 - val_loss: 0.1039\n",
      "Epoch 46/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1053 - val_loss: 0.1034\n",
      "Epoch 47/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1048 - val_loss: 0.1030\n",
      "Epoch 48/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1044 - val_loss: 0.1025\n",
      "Epoch 49/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1039 - val_loss: 0.1021\n",
      "Epoch 50/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1035 - val_loss: 0.1018\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fae88710d10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.fit(x_train, x_train,\n",
    "                nb_epoch=50,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 在测试集上进行编码和解码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# encode and decode some digits\n",
    "# note that we take them from the *test* set\n",
    "encoded_imgs = encoder.predict(x_test)\n",
    "decoded_imgs = decoder.predict(encoded_imgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 显示结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sTA9Qv9+XJIPX3C16qaBXVS6XQz6fRzgcljE5diPYEZlOp6/+MPkUYLeQ0u7D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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3a424f0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 10  # how many digits we will display\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(x_test[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + 1 + n)\n",
    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.构建编码表示上添加稀疏约束的自编码器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import regularizers\n",
    "\n",
    "encoding_dim = 32\n",
    "\n",
    "input_img = Input(shape=(784,))\n",
    "# add a Dense layer with a L1 activity regularizer\n",
    "encoded = Dense(encoding_dim, activation='relu',\n",
    "                activity_regularizer=regularizers.activity_l1(10e-5))(input_img)\n",
    "decoded = Dense(784, activation='sigmoid')(encoded)\n",
    "\n",
    "autoencoder = Model(input=input_img, output=decoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_img = Input(shape=(784,))\n",
    "encoded = Dense(128, activation='relu')(input_img)\n",
    "encoded = Dense(64, activation='relu')(encoded)\n",
    "encoded = Dense(32, activation='relu')(encoded)\n",
    "\n",
    "decoded = Dense(64, activation='relu')(encoded)\n",
    "decoded = Dense(128, activation='relu')(decoded)\n",
    "decoded = Dense(784, activation='sigmoid')(decoded)\n",
    "\n",
    "autoencoder = Model(input=input_img, output=decoded)\n",
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.3401 - val_loss: 0.2627\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.2541 - val_loss: 0.2413\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.2296 - val_loss: 0.2164\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.2068 - val_loss: 0.1989\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1940 - val_loss: 0.1868\n",
      "Epoch 6/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1842 - val_loss: 0.1792\n",
      "Epoch 7/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1765 - val_loss: 0.1711\n",
      "Epoch 8/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1704 - val_loss: 0.1667\n",
      "Epoch 9/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1658 - val_loss: 0.1616\n",
      "Epoch 10/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1616 - val_loss: 0.1597\n",
      "Epoch 11/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1579 - val_loss: 0.1546\n",
      "Epoch 12/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1545 - val_loss: 0.1516\n",
      "Epoch 13/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1513 - val_loss: 0.1482\n",
      "Epoch 14/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1485 - val_loss: 0.1452\n",
      "Epoch 15/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1462 - val_loss: 0.1431\n",
      "Epoch 16/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1437 - val_loss: 0.1433\n",
      "Epoch 17/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1417 - val_loss: 0.1397\n",
      "Epoch 18/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1398 - val_loss: 0.1375\n",
      "Epoch 19/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1380 - val_loss: 0.1374\n",
      "Epoch 20/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1363 - val_loss: 0.1339\n",
      "Epoch 21/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1347 - val_loss: 0.1323\n",
      "Epoch 22/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1334 - val_loss: 0.1309\n",
      "Epoch 23/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1320 - val_loss: 0.1303\n",
      "Epoch 24/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1307 - val_loss: 0.1288\n",
      "Epoch 25/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1295 - val_loss: 0.1287\n",
      "Epoch 26/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1284 - val_loss: 0.1279\n",
      "Epoch 27/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1271 - val_loss: 0.1255\n",
      "Epoch 28/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1261 - val_loss: 0.1236\n",
      "Epoch 29/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1250 - val_loss: 0.1220\n",
      "Epoch 30/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1238 - val_loss: 0.1225\n",
      "Epoch 31/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1229 - val_loss: 0.1230\n",
      "Epoch 32/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1220 - val_loss: 0.1196\n",
      "Epoch 33/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1212 - val_loss: 0.1200\n",
      "Epoch 34/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1202 - val_loss: 0.1188\n",
      "Epoch 35/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1193 - val_loss: 0.1191\n",
      "Epoch 36/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1187 - val_loss: 0.1170\n",
      "Epoch 37/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1178 - val_loss: 0.1163\n",
      "Epoch 38/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1171 - val_loss: 0.1156\n",
      "Epoch 39/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1166 - val_loss: 0.1145\n",
      "Epoch 40/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1160 - val_loss: 0.1136\n",
      "Epoch 41/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1155 - val_loss: 0.1132\n",
      "Epoch 42/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1150 - val_loss: 0.1153\n",
      "Epoch 43/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1145 - val_loss: 0.1129\n",
      "Epoch 44/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1140 - val_loss: 0.1129\n",
      "Epoch 45/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1135 - val_loss: 0.1118\n",
      "Epoch 46/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1131 - val_loss: 0.1118\n",
      "Epoch 47/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1126 - val_loss: 0.1117\n",
      "Epoch 48/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1122 - val_loss: 0.1117\n",
      "Epoch 49/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1117 - val_loss: 0.1103\n",
      "Epoch 50/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1114 - val_loss: 0.1093\n",
      "Epoch 51/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1109 - val_loss: 0.1096\n",
      "Epoch 52/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1107 - val_loss: 0.1099\n",
      "Epoch 53/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1102 - val_loss: 0.1090\n",
      "Epoch 54/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1098 - val_loss: 0.1083\n",
      "Epoch 55/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1095 - val_loss: 0.1077\n",
      "Epoch 56/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1091 - val_loss: 0.1077\n",
      "Epoch 57/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1089 - val_loss: 0.1081\n",
      "Epoch 58/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1085 - val_loss: 0.1082\n",
      "Epoch 59/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1082 - val_loss: 0.1073\n",
      "Epoch 60/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1078 - val_loss: 0.1066\n",
      "Epoch 61/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1075 - val_loss: 0.1059\n",
      "Epoch 62/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1072 - val_loss: 0.1053\n",
      "Epoch 63/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1069 - val_loss: 0.1063\n",
      "Epoch 64/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1067 - val_loss: 0.1047\n",
      "Epoch 65/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1064 - val_loss: 0.1044\n",
      "Epoch 66/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1060 - val_loss: 0.1048\n",
      "Epoch 67/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1057 - val_loss: 0.1039\n",
      "Epoch 68/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1055 - val_loss: 0.1039\n",
      "Epoch 69/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1052 - val_loss: 0.1043\n",
      "Epoch 70/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1050 - val_loss: 0.1042\n",
      "Epoch 71/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1047 - val_loss: 0.1036\n",
      "Epoch 72/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1044 - val_loss: 0.1033\n",
      "Epoch 73/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1042 - val_loss: 0.1040\n",
      "Epoch 74/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1040 - val_loss: 0.1024\n",
      "Epoch 75/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1037 - val_loss: 0.1025\n",
      "Epoch 76/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1035 - val_loss: 0.1020\n",
      "Epoch 77/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1031 - val_loss: 0.1019\n",
      "Epoch 78/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1030 - val_loss: 0.1022\n",
      "Epoch 79/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1028 - val_loss: 0.1024\n",
      "Epoch 80/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1026 - val_loss: 0.1011\n",
      "Epoch 81/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1023 - val_loss: 0.1004\n",
      "Epoch 82/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1020 - val_loss: 0.1015\n",
      "Epoch 83/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1018 - val_loss: 0.1005\n",
      "Epoch 84/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1016 - val_loss: 0.1018\n",
      "Epoch 85/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.1013 - val_loss: 0.0995\n",
      "Epoch 86/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1013 - val_loss: 0.0998\n",
      "Epoch 87/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1010 - val_loss: 0.0995\n",
      "Epoch 88/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1008 - val_loss: 0.1008\n",
      "Epoch 89/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1006 - val_loss: 0.0995\n",
      "Epoch 90/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1005 - val_loss: 0.1007\n",
      "Epoch 91/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1003 - val_loss: 0.0990\n",
      "Epoch 92/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.1001 - val_loss: 0.0983\n",
      "Epoch 93/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.0999 - val_loss: 0.0992\n",
      "Epoch 94/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.0999 - val_loss: 0.0994\n",
      "Epoch 95/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.0996 - val_loss: 0.0977\n",
      "Epoch 96/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.0994 - val_loss: 0.0983\n",
      "Epoch 97/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.0994 - val_loss: 0.0981\n",
      "Epoch 98/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.0991 - val_loss: 0.0974\n",
      "Epoch 99/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.0989 - val_loss: 0.0971\n",
      "Epoch 100/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.0988 - val_loss: 0.0971\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd354149b10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.fit(x_train, x_train,\n",
    "                nb_epoch=100,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 在测试集上进行编码和解码\n",
    "该模型与之前的模型明显不同在于编码的稀疏结构，该模型在10000个测试数据上encoded_imgs.mean()得到了3.33的值，而之前的模型得到了7.30值，说明该模型产生的编码更加的稀疏。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# encode and decode some digits\n",
    "# note that we take them from the *test* set\n",
    "encoded_imgs = encoder.predict(x_test)\n",
    "decoded_imgs = decoder.predict(encoded_imgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 显示结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H40G5XIbVasVgMEC325W/r5+jlwfVf88wDEmiTSaTSCaTSCQS4qfpcrngdrtvKG7UGoMN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3481e5450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 10  # how many digits we will display\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(x_test[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + 1 + n)\n",
    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.构建多隐藏层自编码器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_img = Input(shape=(784,))\n",
    "encoded = Dense(128, activation='relu')(input_img)\n",
    "encoded = Dense(64, activation='relu')(encoded)\n",
    "encoded = Dense(32, activation='relu')(encoded)\n",
    "\n",
    "decoded = Dense(64, activation='relu')(encoded)\n",
    "decoded = Dense(128, activation='relu')(decoded)\n",
    "decoded = Dense(784, activation='sigmoid')(decoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autoencoder = Model(input=input_img, output=decoded)\n",
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
    "\n",
    "autoencoder.fit(x_train, x_train,\n",
    "                nb_epoch=100,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  },
  "ssap_exp_config": {
   "error_alert": "Error Occurs!",
   "initial": [],
   "max_iteration": 1000,
   "recv_id": "",
   "running": [],
   "summary": [],
   "version": "1.1.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
